Stress corrosion cracking (SCC) of austenitic alloys in water-cooled nuclear reactors is one of the most significant component degradation phenomena. SCC occurs due to the synergistic effects of tensile stresses, environment and material susceptibility. For reactor life extension, understanding this mechanism is essential. The methodology most frequently employed to investigate SCC cracking is an experimental one, requiring lengthy and costly tests of several thousand hours. Furthermore, the considerable number of critical parameters that influence susceptibility to SCC cracking and coupling effects have resulted in test grids increasing in length and complexity. This thesis proposes a novel approach based on the use of interpretable models that are driven by the artificial intelligence of fuzzy logic. The aim is to reduce the length and cost of research activities by focusing on relevant tests and parameters that can improve environmental performance. The key issues here will be to add the performance of artificial intelligence to the experimental approach, with the aim of defining susceptibility domains for the initiation of SCC cracks as a function of the critical parameters identified in the model, and providing data for the development of new materials by additive manufacturing. The thesis will develop a numerical model that can be used as guidance in decision-making regarding the stress corrosion mechanism. The future PhD student will also carry out experimental work to validate this new numerical approach.